In the last 3 years, the world has seen a tremendous increase in the amount of structured and unstructured data captured. To reduce risk and confidently comply with regulations like GDPR, CCAR, and BCBS239, industries need to safeguard the quality of various data types, including customer product, financial, and asset data, at data entry, in motion or at target.
Also there should be a proper data minimization, pseudomisation and finally deleting the data.
They need to apply quality rules and processes to all their data, regardless of whether it resides on-premises, in the cloud, or in Hadoop, at data entry, in motion, or at target.
Business and IT collaboration the answer
Because data lives everywhere, data governance cannot be solved in one corner of the organization. An organization needs to engage all constituencies, IT and business, to effectively and collectively safeguard the trustworthiness and quality of their data to power key business initiatives. By integrating self-service business tools with the solutions that your IT team uses, you can give your whole organization the technology that they need for a successful data governance program. We are here to assist your organization comply with your data governance needs.
Data Governance lifecycle
BFTTT offers a complete end-to-end data governance solution to integrate a self-service tool with the powerful technology that IT teams use, enabling business and IT users to consistently and collaboratively improve the trustworthiness and quality of their data to power key business initiatives IT focus on the reliability of the data, while assisting your organization to visualize your data lineage along with scorecards and the key stakeholders that are important with each regulation. Integrating a business tool with the solutions that your IT team uses, we will be able to give your whole organization the technology that you need for a successful data governance program.
BFTTT has identified essentially, 4 data governance best practices when launching a data governance program:
1. Focus on the operating model
The operating model is the basis for any data governance program. It includes activities such as defining enterprise roles and responsibilities across the different lines of business. The idea is to establish an enterprise governance structure. Depending on the type of organization, the structure could be centralized (if a central authority manages everything), decentralized (if operated by a decentralized or group of authorities), or federated (if controlled by independent or multiple groups with little or no shared ownership).
Normally, we start our initial engagement by interviewing different leaders from each line of business such as finance, insurance, sales, and marketing. This exercise is to identify key representatives, one for the business track and other for the technical track. Sometimes these personas are also referred to as business stewards and technical stewards supporting parallel universes from both business perspective (owners of data), and information technology (owners of the infrastructure supporting data). Stewards form groups that roll up to the head of business lines, and business lines roll up to the leaders of business and IT.
As a data governance best practice, our client shared the idea of creating an enterprise data governance structure and formed a corporate data governance council reporting up to the Chief Data Officer.
It is important to define the realm of ownership across your organization. Determining authority will help socialize your data governance program and establish intelligence structure to tackle data programs as one unit of force.
Members of the business and IT form different groups and align to a reporting structure often referred to as the data governance council or the data stewardship committee. It is this council or committee where the majority of everyday data decisions are discussed and disseminated across the organization. The data governance council ensures formalized ownership, and determines the right tools and technology to support stewards so they can perform their job efficiently.
2. Identify data domains
After establishing the data governance structure, the next step is to determine the data domains for each line of business. The most famous examples include customer, vendor, and product data domains. Depending on the type of industry, we come across different kinds of domains. But everything boils down to identifying domains and capturing information about a business and its consumers.
Normally we consider the following artefact in regards to customer, vendor, and product.
- Data owners
- Business glossaries
- Data dictionaries
- Business processes
- Data catalogues
- Reports catalogues
- Data quality scorecards
- Systems and applications
- Policies and standards
Typically, the identification of a data domain starts with a business need or problem.
For example, one of our clients, a major financial services institution, approached us with the following operational requirements:
- Increase customer experience
- Control over validating customer needs
- Manage customer usage
- Increase upsell on storage billing cycles
Note: Data governance is about people, processes, and technology. It can be enabled by identifying a data governance structure, assigning roles and responsibilities, and managing key information assets through a technology platform.
Requirements were tied to the business problem our client was facing: they had to control visibility and understanding around its customers. Data was spread across multiple systems and applications with no defined ownership.
We helped create ownership by identifying key stakeholders, business processes, and datasets related to the customer domain and established control around its lifecycle. The idea is to have a clear understanding of where data comes from, who owns it, and when changes are made, who should be involved (all of which can be clearly defined and managed within the Enterprise Data Governance Center platform).
3. Identify critical data elements within the data domains
After defining the data domains, now, we are standing at the pinnacle. From here, evidently, we see data domains touching 10s, and 100s of systems and applications containing key reports, critical data elements, business processes, and more. Obviously, we do not want to boil the ocean by focusing on all the data artefacts at once. Instead, we should only identify what is critical to the business.
For example, working with one of our federal government agency, their data governance initiative was to attain commonality across the enterprise by creating a centralized platform to manage and control changes and providing visibility into critical data assets. A platform to serve as a vibrant ecosystem, fostering collaboration, lifecycle management, and retaining audit logs for the past vs. future analysis.
4. Define control measurements
We explained data governance structures, data domains, and identifying critical data elements. The next step is to set and maintain controls to sustain the data governance program. After delivering data governance solutions across multiple industries including banking, healthcare, insurance, government, retail, manufacturing, and more, we have explained that data governance is not a one-time project. It is an ongoing program to fuel data-driven decision making and creating opportunities for business. It prepares an organization to meet business standards. Control measurements include the following key activities:
- Define automated workflow processes and thresholds for approval, escalation, review, voting, issue management and more
- Apply workflow processes to the governance structure, data domains, and critical data elements
- Develop reporting on the progress of steps 1 through step 4
- Capture feedback through automated workflow processes
For example, one of our technology customers started with data governance began by defining ownership, roles, and responsibilities, defining business data definitions and applying workflow processes to include data stewards in the change management process.
5. Data Governance Best Practices
There is more to the four data governance best practices as mentioned above for kicking off an enterprise data governance program. And depending on the industry, there are different approaches. The above steps stand valid for establishing effective information governance, which is the foundation for better quality, privacy, security, and many business intelligence and MDM programs. Data governance will help us prepare for the growing trends such as AI, Hadoop, IoT, and blockchain.
Does it sound like a fit for your organization? Contact us for a free consultation.